imagine.observables package

Submodules

imagine.observables.dataset module

Datasets are auxiliary classes used to facilitate the reading and inclusion of observational data in the IMAGINE pipeline

class imagine.observables.dataset.Dataset

Bases: imagine.tools.class_tools.BaseClass

Base class for writing helpers to convert arbitrary observational datasets into IMAGINE’s standardized format

REQ_ATTRS = ['NAME']

data

Array in the shape (1, N)

frequency

key

Key used in the Observables_dictionary

name

var

class imagine.observables.dataset.TabularDataset(data, name, *, data_col=None, units=None, coords_type=None, lon_col='lon', lat_col='lat', x_col='x', y_col='y', z_col='z', err_col=None, frequency=None, tag=None)

Bases: imagine.observables.dataset.Dataset

Base class for tabular datasets, where the data is input in either in a Python dictionary-like object (dict, astropy.table.Table, pandas.DataFrame, etc).

Parameters:

• data (dict_like) – Can be a dict, astropy.table.Table, pandas.DataFrame, or similar object containing the data.

• name (str) – Standard name of this type of observable. E.g. ‘fd’, ‘sync’, ‘dm’.

• Optional

• ——–

• data_col (str or None. Default: None) – Key used to access the relevant dataset from the provided data (i.e. data[data_column]). If None, this value is equal to name.

• units (Unit object, str or None. Default: None) – Units used for the data. If None, the units are inferred from the given data_column.

• coords_type ({‘galactic’; ‘cartesian’} or None. Default: None) – Type of coordinates used. If None, type is inferred from present coordinate columns.

• lon_col, lat_col (str. Default: (‘lon’, ‘lat’)) – Key used to access the Galactic longitudes/latitudes (in deg) from data.

• x_col, y_col, z_col (str. Default: (‘x’, ‘y’, ‘z’)) – Keys used to access the coordinates (in kpc) from data.

• err_col (str or None. Default: None) – The key used for accessing the error for the data values. If None, no errors are used.

• frequency (Quantity object or None. Default: None) – Frequency of the measurement (if relevant)

• tag (str or None. Default: None) –

  Extra information associated with the observable.

  • ‘I’ - total intensity (in unit K-cmb)
  • ‘Q’ - Stokes Q (in unit K-cmb, IAU convention)
  • ‘U’ - Stokes U (in unit K-cmb, IAU convention)
  • ‘PI’ - polarisation intensity (in unit K-cmb)
  • ‘PA’ - polarisation angle (in unit rad, IAU convention)

class imagine.observables.dataset.HEALPixDataset(data, error=None, cov=None, Nside=None)

Bases: imagine.observables.dataset.Dataset

Base class for HEALPix datasets, which are input as a simple 1D-array without explicit coordinate information

class imagine.observables.dataset.ImageDataset(data, name, lon_min, lon_max, lat_min, lat_max, object_id=None, units=None, error=None, cov=None, frequency=None, tag=None)

Bases: imagine.observables.dataset.Dataset

Class for simple non-full-sky image data

class imagine.observables.dataset.FaradayDepthHEALPixDataset(data, error=None, cov=None, Nside=None)

Bases: imagine.observables.dataset.HEALPixDataset

Stores a Faraday depth map into an IMAGINE-compatible dataset

Parameters:

• data (numpy.ndarray) – 1D-array containing the HEALPix map
• Nside (int, optional) – For extra internal consistency checking. If Nside is present, it will be checked whether \(12\times N_{side}^2\) matches
• error (float or array) – If errors are uncorrelated, this can be used to specify them (a diagonal covariance matrix will then be constructed).
• cov (numpy.ndarray) – 2D-array containing the covariance matrix

data

Data in ObservablesDict-compatible shape

key

Standard key associated with this observable

NAME = 'fd'

class imagine.observables.dataset.SynchrotronHEALPixDataset(data, frequency, typ, **kwargs)

Bases: imagine.observables.dataset.HEALPixDataset

Stores a synchrotron emission map into an IMAGINE-compatible dataset. This can be Stokes parameters, total and polarised intensity, and polarisation angle.

The parameter typ and the units of the map in data must follow:

• ‘I’ - total intensity (in unit K-cmb)
• ‘Q’ - Stokes Q (in unit K-cmb, IAU convention)
• ‘U’ - Stokes U (in unit K-cmb, IAU convention)
• ‘PI’ - polarisation intensity (in unit K-cmb)
• ‘PA’ - polarisation angle (in unit rad, IAU convention)

Parameters:

• data (numpy.ndarray) – 1D-array containing the HEALPix map
• frequency (astropy.units.Quantity) – Frequency of the measurement (if relevant)
• Nside (int, optional) – For extra internal consistency checking. If Nside is present, it will be checked whether \(12\times N_{side}^2\) matches data.size
• typ (str) – The type of map being supplied in data.
• error (float or array) – If errors are uncorrelated, this can be used to specify them (a diagonal covariance matrix will then be constructed).
• cov (numpy.ndarray) – 2D-array containing the covariance matrix

data

Data in ObservablesDict-compatible shape

key

Standard key associated with this observable

NAME = 'sync'

class imagine.observables.dataset.DispersionMeasureHEALPixDataset(data, error=None, cov=None, Nside=None)

Bases: imagine.observables.dataset.HEALPixDataset

Stores a dispersion measure map into an IMAGINE-compatible dataset

Parameters:

• data (numpy.ndarray) – 1D-array containing the HEALPix map
• Nside (int, optional) – For extra internal consistency checking. If Nside is present, it will be checked whether \(12\times N_{side}^2\) matches
• error (float or array) – If errors are uncorrelated, this can be used to specify them (a diagonal covariance matrix will then be constructed).
• cov (numpy.ndarray) – 2D-array containing the covariance matrix

data

Data in ObservablesDict-compatible shape

key

Standard key associated with this observable

NAME = 'dm'

imagine.observables.observable module

In the Observable class we define three data types, i.e., - ‘measured’ - ‘simulated’ - ‘covariance’ where ‘measured’ indicates the hosted data is from measurements, which has a single realization, ‘simulated’ indicates the hosted data is from simulations, which has multiple realizations, ‘covariance’ indicates the hosted data is a covariance matrix, which has a single realization but by default should not be stored/read/written by a single computing node.

‘measred’ data puts its identical copies on all computing nodes, which means each node has a full storage of ‘measured’ data.

‘simulated’ data puts different realizations on different nodes, which means each node has part of the full realizations, but at least a full version of one single realization.

‘covariance’ data distributes itself into all computing nodes, which means to have a full set of ‘covariance’ data, we have to collect pieces from all the computing nodes.

class imagine.observables.observable.Observable(data=None, dtype=None, coords=None, otype=None)

Bases: object

Observable class is designed for storing/manipulating distributed information. For the testing suits, please turn to “imagine/tests/observable_tests.py”.

Parameters:

• data (numpy.ndarray) – distributed/copied data
• dtype (str) – Data type, must be either: ‘measured’, ‘simulated’ or ‘covariance’
• otype (str) – Observable type, must be either: ‘HEALPix’, ‘tabular’ or ‘plain’

append(new_data)

appending new data happens only to SIMULATED dtype the new data to be appended should also be distributed which makes the appending operation naturally in parallel

rewrite flag will be switched off once rewriten has been performed

data

Data stored in the local processor

dtype

‘measured’, ‘simulated’ or ‘covariance’

Type:Data type, can be either

ensemble_mean

global_data

Data gathered from ALL processors (numpy.ndarray, read-only). Note that only master node hosts the global data, while slave nodes hosts None.

rw_flag

Rewriting flag, if true, append method will perform rewriting

shape

Shape of the GLOBAL array, i.e. considering all processors (numpy.ndarray, read-only).

size

Local data size (int, read-only) this size means the dimension of input data not the sample size of realizations

var

The stored variance, if the Observable is a variance or covariance

imagine.observables.observable_dict module

For convenience we define dictionary of Observable objects as ObservableDict from which one can define define the classes Measurements, Covariances, Simulations and Masks, which can be used to store:

• measured data sets
• measured/simulated covariances
• simulated ensemble sets
• mask “maps”

Conventions for observables key values

• Faraday depth: (‘fd’, None, size/Nside, None)
• Dispersion measure: (‘dm’, None, size/Nsize, None)

• Synchrotron emission: (‘sync’, freq, size/Nside, tag)

  where tag stands for:

  • ‘I’ - total intensity (in unit K-cmb)
  • ‘Q’ - Stokes Q (in unit K-cmb, IAU convention)
  • ‘U’ - Stokes U (in unit K-cmb, IAU convention)
  • ‘PI’ - polarisation intensity (in unit K-cmb)
  • ‘PA’ - polarisation angle (in unit rad, IAU convention)

Remarks:

• freq, frequency in GHz

• str(pix/nside) stores either Healpix Nside, or just number of

  pixels/points

Masking convention

masked area associated with pixel value 0, unmasked area with pixel value 1

Masking

After applying a mask, the Observables change otype from ‘HEALPix’ to ‘plain’.

class imagine.observables.observable_dict.ObservableDict(*datasets)

Bases: imagine.tools.class_tools.BaseClass

Base class from which Measurements, Covariances, Simulations and class Masks classes are derived.

Parameters:datasets (imagine.observables.Dataset, optional) – If present, Datasets that are appended to this ObservableDict object after initialization.

append(dataset=None, *, name=None, data=None, cov_data=None, otype=None, coords=None)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

keys()

show(**kwargs)

Shows the contents of this ObservableDict using imagine.tools.visualization.show_observable_dict()

archive

class imagine.observables.observable_dict.Masks(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores HEALPix mask maps.

After the Masks dictionary is assembled it can be applied to any other observables dictionary to return a dictionary containing masked maps (see below).

Example

>>> import imagine.observables as obs
>>> import numpy as np
>>> meas, cov, mask = obs.Measurements(), obs.Covariances(), obs.Masks()
>>> key = ('test','nan','4','nan')
>>> meas.append(name=key, data=np.array([[1,2,3,4.]]), otype='plain')
>>> mask.append(name=key, data=np.array([[1,2,3,4.]]), otype='plain')
>>> masked_meas = mask(meas)
>>> print(masked_meas[('test','nan','2','nan')].data)
[[1. 2.]]
>>> cov.append(name=key, cov_data=np.diag((1,2,3,4.)), otype='plain')
>>> masked_cov = mask(cov)
>>> print(masked_cov[('test',None, 2 ,None)].data)
[[1. 0.]
 [0. 2.]]

__call__(observable_dict)

Applies the masks

Parameters:observable_dict (imagine.observables.ObservableDict) – Dictionary containing (some) entries where one wants to apply the masks. Returns:masked_dict – New observables dictionary containing masked entries (any entries in the original dictionary for which no mask was specified are referenced in masked_dict without modification). Return type:imagine.observables.ObservableDict

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

class imagine.observables.observable_dict.Measurements(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores observational data.

Parameters:datasets (imagine.observables.Dataset, optional) – If present, Datasets that are appended to this ObservableDict object after initialization.

cov

The Covariances object associated with these measurements.

Type:imagine.observables.observable_dict.Covariances

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

class imagine.observables.observable_dict.Simulations(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores simulated ensemble sets

See imagine.observables.observable_dict module documentation for further details.

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

estimate_covariances(cov_est=<function oas_cov>)

Produces a Covariances object based on the present Simulations

Parameters:cov_est (func) – A function that computes the covariance given a matrix of data Returns:covs – IMAGINE Covariances object Return type:imagine.observables.Covariances

sub_sim(indices)

Creates a new Simulations object based on a subset of the ensemble of a larger Simulations.

Parameters:indices – A tuple of indices numbers, a slice object or a boolean array which will be used to select the data for the sub-simulation Returns:sims_subset – The selected sub-simulation Return type:imagine.observables.Simulations

class imagine.observables.observable_dict.Covariances(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores observational covariances

See imagine.observables.observable_dict module documentation for further details.

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

show_variances(**kwargs)

Shows the contents of this ObservableDict using imagine.tools.visualization.show_observable_dict()

Module contents

class imagine.observables.Dataset

Bases: imagine.tools.class_tools.BaseClass

Base class for writing helpers to convert arbitrary observational datasets into IMAGINE’s standardized format

REQ_ATTRS = ['NAME']

data

Array in the shape (1, N)

frequency

key

Key used in the Observables_dictionary

name

var

class imagine.observables.TabularDataset(data, name, *, data_col=None, units=None, coords_type=None, lon_col='lon', lat_col='lat', x_col='x', y_col='y', z_col='z', err_col=None, frequency=None, tag=None)

Bases: imagine.observables.dataset.Dataset

Base class for tabular datasets, where the data is input in either in a Python dictionary-like object (dict, astropy.table.Table, pandas.DataFrame, etc).

Parameters:

• data (dict_like) – Can be a dict, astropy.table.Table, pandas.DataFrame, or similar object containing the data.

• name (str) – Standard name of this type of observable. E.g. ‘fd’, ‘sync’, ‘dm’.

• Optional

• ——–

• data_col (str or None. Default: None) – Key used to access the relevant dataset from the provided data (i.e. data[data_column]). If None, this value is equal to name.

• units (Unit object, str or None. Default: None) – Units used for the data. If None, the units are inferred from the given data_column.

• coords_type ({‘galactic’; ‘cartesian’} or None. Default: None) – Type of coordinates used. If None, type is inferred from present coordinate columns.

• lon_col, lat_col (str. Default: (‘lon’, ‘lat’)) – Key used to access the Galactic longitudes/latitudes (in deg) from data.

• x_col, y_col, z_col (str. Default: (‘x’, ‘y’, ‘z’)) – Keys used to access the coordinates (in kpc) from data.

• err_col (str or None. Default: None) – The key used for accessing the error for the data values. If None, no errors are used.

• frequency (Quantity object or None. Default: None) – Frequency of the measurement (if relevant)

• tag (str or None. Default: None) –

  Extra information associated with the observable.

  • ‘I’ - total intensity (in unit K-cmb)
  • ‘Q’ - Stokes Q (in unit K-cmb, IAU convention)
  • ‘U’ - Stokes U (in unit K-cmb, IAU convention)
  • ‘PI’ - polarisation intensity (in unit K-cmb)
  • ‘PA’ - polarisation angle (in unit rad, IAU convention)

class imagine.observables.HEALPixDataset(data, error=None, cov=None, Nside=None)

Bases: imagine.observables.dataset.Dataset

Base class for HEALPix datasets, which are input as a simple 1D-array without explicit coordinate information

class imagine.observables.ImageDataset(data, name, lon_min, lon_max, lat_min, lat_max, object_id=None, units=None, error=None, cov=None, frequency=None, tag=None)

Bases: imagine.observables.dataset.Dataset

Class for simple non-full-sky image data

class imagine.observables.FaradayDepthHEALPixDataset(data, error=None, cov=None, Nside=None)

Bases: imagine.observables.dataset.HEALPixDataset

Stores a Faraday depth map into an IMAGINE-compatible dataset

Parameters:

• data (numpy.ndarray) – 1D-array containing the HEALPix map
• Nside (int, optional) – For extra internal consistency checking. If Nside is present, it will be checked whether \(12\times N_{side}^2\) matches
• error (float or array) – If errors are uncorrelated, this can be used to specify them (a diagonal covariance matrix will then be constructed).
• cov (numpy.ndarray) – 2D-array containing the covariance matrix

data

Data in ObservablesDict-compatible shape

key

Standard key associated with this observable

NAME = 'fd'

class imagine.observables.SynchrotronHEALPixDataset(data, frequency, typ, **kwargs)

Bases: imagine.observables.dataset.HEALPixDataset

Stores a synchrotron emission map into an IMAGINE-compatible dataset. This can be Stokes parameters, total and polarised intensity, and polarisation angle.

The parameter typ and the units of the map in data must follow:

• ‘I’ - total intensity (in unit K-cmb)
• ‘Q’ - Stokes Q (in unit K-cmb, IAU convention)
• ‘U’ - Stokes U (in unit K-cmb, IAU convention)
• ‘PI’ - polarisation intensity (in unit K-cmb)
• ‘PA’ - polarisation angle (in unit rad, IAU convention)

Parameters:

• data (numpy.ndarray) – 1D-array containing the HEALPix map
• frequency (astropy.units.Quantity) – Frequency of the measurement (if relevant)
• Nside (int, optional) – For extra internal consistency checking. If Nside is present, it will be checked whether \(12\times N_{side}^2\) matches data.size
• typ (str) – The type of map being supplied in data.
• error (float or array) – If errors are uncorrelated, this can be used to specify them (a diagonal covariance matrix will then be constructed).
• cov (numpy.ndarray) – 2D-array containing the covariance matrix

data

Data in ObservablesDict-compatible shape

key

Standard key associated with this observable

NAME = 'sync'

class imagine.observables.DispersionMeasureHEALPixDataset(data, error=None, cov=None, Nside=None)

Bases: imagine.observables.dataset.HEALPixDataset

Stores a dispersion measure map into an IMAGINE-compatible dataset

Parameters:

• data (numpy.ndarray) – 1D-array containing the HEALPix map
• Nside (int, optional) – For extra internal consistency checking. If Nside is present, it will be checked whether \(12\times N_{side}^2\) matches
• error (float or array) – If errors are uncorrelated, this can be used to specify them (a diagonal covariance matrix will then be constructed).
• cov (numpy.ndarray) – 2D-array containing the covariance matrix

data

Data in ObservablesDict-compatible shape

key

Standard key associated with this observable

NAME = 'dm'

class imagine.observables.Observable(data=None, dtype=None, coords=None, otype=None)

Bases: object

Observable class is designed for storing/manipulating distributed information. For the testing suits, please turn to “imagine/tests/observable_tests.py”.

Parameters:

• data (numpy.ndarray) – distributed/copied data
• dtype (str) – Data type, must be either: ‘measured’, ‘simulated’ or ‘covariance’
• otype (str) – Observable type, must be either: ‘HEALPix’, ‘tabular’ or ‘plain’

append(new_data)

appending new data happens only to SIMULATED dtype the new data to be appended should also be distributed which makes the appending operation naturally in parallel

rewrite flag will be switched off once rewriten has been performed

data

Data stored in the local processor

dtype

‘measured’, ‘simulated’ or ‘covariance’

Type:Data type, can be either

ensemble_mean

global_data

Data gathered from ALL processors (numpy.ndarray, read-only). Note that only master node hosts the global data, while slave nodes hosts None.

rw_flag

Rewriting flag, if true, append method will perform rewriting

shape

Shape of the GLOBAL array, i.e. considering all processors (numpy.ndarray, read-only).

size

Local data size (int, read-only) this size means the dimension of input data not the sample size of realizations

var

The stored variance, if the Observable is a variance or covariance

class imagine.observables.ObservableDict(*datasets)

Bases: imagine.tools.class_tools.BaseClass

Base class from which Measurements, Covariances, Simulations and class Masks classes are derived.

Parameters:datasets (imagine.observables.Dataset, optional) – If present, Datasets that are appended to this ObservableDict object after initialization.

append(dataset=None, *, name=None, data=None, cov_data=None, otype=None, coords=None)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

keys()

show(**kwargs)

Shows the contents of this ObservableDict using imagine.tools.visualization.show_observable_dict()

archive

class imagine.observables.Masks(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores HEALPix mask maps.

After the Masks dictionary is assembled it can be applied to any other observables dictionary to return a dictionary containing masked maps (see below).

Example

>>> import imagine.observables as obs
>>> import numpy as np
>>> meas, cov, mask = obs.Measurements(), obs.Covariances(), obs.Masks()
>>> key = ('test','nan','4','nan')
>>> meas.append(name=key, data=np.array([[1,2,3,4.]]), otype='plain')
>>> mask.append(name=key, data=np.array([[1,2,3,4.]]), otype='plain')
>>> masked_meas = mask(meas)
>>> print(masked_meas[('test','nan','2','nan')].data)
[[1. 2.]]
>>> cov.append(name=key, cov_data=np.diag((1,2,3,4.)), otype='plain')
>>> masked_cov = mask(cov)
>>> print(masked_cov[('test',None, 2 ,None)].data)
[[1. 0.]
 [0. 2.]]

__call__(observable_dict)

Applies the masks

Parameters:observable_dict (imagine.observables.ObservableDict) – Dictionary containing (some) entries where one wants to apply the masks. Returns:masked_dict – New observables dictionary containing masked entries (any entries in the original dictionary for which no mask was specified are referenced in masked_dict without modification). Return type:imagine.observables.ObservableDict

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

class imagine.observables.Measurements(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores observational data.

Parameters:datasets (imagine.observables.Dataset, optional) – If present, Datasets that are appended to this ObservableDict object after initialization.

cov

The Covariances object associated with these measurements.

Type:imagine.observables.observable_dict.Covariances

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

class imagine.observables.Simulations(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores simulated ensemble sets

See imagine.observables.observable_dict module documentation for further details.

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

estimate_covariances(cov_est=<function oas_cov>)

Produces a Covariances object based on the present Simulations

Parameters:cov_est (func) – A function that computes the covariance given a matrix of data Returns:covs – IMAGINE Covariances object Return type:imagine.observables.Covariances

sub_sim(indices)

Creates a new Simulations object based on a subset of the ensemble of a larger Simulations.

Parameters:indices – A tuple of indices numbers, a slice object or a boolean array which will be used to select the data for the sub-simulation Returns:sims_subset – The selected sub-simulation Return type:imagine.observables.Simulations

class imagine.observables.Covariances(*datasets)

Bases: imagine.observables.observable_dict.ObservableDict

Stores observational covariances

See imagine.observables.observable_dict module documentation for further details.

append(*args, **kwargs)

Adds/updates name and data

Parameters:

• dataset (imagine.observables.dataset.Dataset) – The IMAGINE dataset already adjusts the format of the data and sets the adequate key. If dataset is present, all other arguments will be ignored.
• name (str tuple) – Should follow the convention: (data-name, data-freq, data-Nside/"tab", ext). If data is independent from frequency, set None. ext can be ‘I’,’Q’,’U’,’PI’,’PA’, None or other customized tags.
• data (numpy.ndarray or imagine.observables.observable.Observable) – distributed/copied numpy.ndarray or Observable
• otype (str) – Type of observable. May be: ‘HEALPix’, for HEALPix-like sky map; ‘tabular’, for tabular data; or ‘plain’ for unstructured data.
• coords (dict) – A dictionary containing the coordinates of tabular data

show_variances(**kwargs)

Shows the contents of this ObservableDict using imagine.tools.visualization.show_observable_dict()